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import gradio as gr
import tensorflow as tf
import gdown
from PIL import Image
input_shape = (32, 32, 3)
resized_shape = (224, 224, 3)
num_classes = 10
labels = {
0: "plane",
1: "car",
2: "bird",
3: "cat",
4: "deer",
5: "dog",
6: "frog",
7: "horse",
8: "ship",
9: "truck",
}
# Download the model file
def download_model():
url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL"
output = "modelV2Lmixed.keras"
gdown.download(url, output, quiet=False)
return output
model_file = download_model()
# Load the model
model = tf.keras.models.load_model(model_file)
# Perform image classification
def predict_class(image):
img = tf.cast(image, tf.float32)
img = tf.image.resize(img, [input_shape[0], input_shape[1]])
img = tf.expand_dims(img, axis=0)
prediction = model.predict(img)
class_index = tf.argmax(prediction[0]).numpy()
predicted_class = labels[class_index]
return predicted_class
# UI Design
def classify_image(image):
predicted_class = predict_class(image)
output = f"<h2>Predicted Class: <span style='text-transform:uppercase';>{predicted_class}</span></h2>"
return output
inputs = gr.inputs.Image(label="Upload an image")
outputs = gr.outputs.HTML()
title = "<h1 style='text-align: center;'>Image Classifier</h1>"
description = "Upload an image and get the predicted class."
gr.Interface(fn=classify_image,
inputs=inputs,
outputs=outputs,
title=title,
examples=["00_plane.jpg", "01_car.jpg", "02_bird.jpg", "03_cat.jpg", "04_deer.jpg"],
css="body {background-image: url('file=./wave.mp4')}",
description=description).launch()
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